Predictive value of artificial intelligence and radiomics for atrial fibrillation recurrence after catheter ablation for pulmonary vein isolation.

ABSTRACT:

Background

Atrial fibrillation (AF) is a common arrhythmia disorder with a high recurrence rate after catheter ablation for pulmonary vein isolation (PVI). Improved preoperative evaluation strategies are needed to enhance prediction accuracy and optimize patient selection for ablation.

Materials and methods

This study included 311 AF patients who underwent catheter ablation for PVI, stratified into recurrence (n = 160) and non-recurrence (n = 151) groups based on postoperative outcome. The deep learning model SwinUNETR was used to segment the heart region and extract the radiomics features of different areas of the heart. A risk prediction model was built using six machine learning methods. Finally, an interpretable analysis method based on radiomics was provid

SUBMITTER: Ma G 

PROVIDER: S-EPMC12579729 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature